Chapter $4$: FINDINGS, ANALYSIS, AND PRODUCT DEVELOPMENT

Technologies Used

$R$ was chosen as the main programming language and basis for this project. The main

reason to use $R$ is that it is very potent for data exploration and visualization by providing a lot of

tools to help simplify the process and provide more freedom due to the vast number of libraries

available all with the goal of making visualizations simple. Another reason to use $R$ is that the

project focuses more on standalone analysis and processing so the more refined statistical tools

in $R$ can be used to help with the vast amount of data wrangling required to get the data ready for

visualization as well as make a dashboard for the analysis to be presented and visualizations to

have interactions with. Some of these tools are the potent libraries available in R like tidyverse

for helping overall with data wrangling and visualization, lubridate for handling date data present

in a lot of the datasets used in this project, leaflet for providing a base for interactive choropleth

maps, and shiny for making it possible to take $R$ visualizations and make them more dynamic on

an interactive web app. One negative aspect that was considered with $R$ was the data size limit$,$

but the scope of the project and the amount of data looked at in research did not exceed the limit$.$

This meant it was safe to use $R.$

In addition to the base language itself, the development environment was also important

for a variety of reasons. RStudio is the main IDE for R and provides a lot of useful features for

both development and deployment of the project such as making it easy to export a project for

version control or quickly test code at multiple stages, so it was chosen to be the base for the

project to run on$.$ Because of the functionality provided between base $R$ and RStudio the

development and deployment environment could be the same which made the version control

and overall handling of the project very simple.